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      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '/content/' # This can be any directory you want to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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            "\n",
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "text": [
            "\n",
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
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            "\n",
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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        },
        {
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            "\n",
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohCvmE9YpCAX"
      },
      "source": [
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-31yzXgbpG6Z"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWzQyWZfpICr"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VYvYiaizEEm"
      },
      "source": [
        "### Without BatchNormalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9S5WW03T0Q1G"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/(255*10000)\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    class neuralnet(nn.Module):\n",
        "        def __init__(self):\n",
        "            super().__init__()\n",
        "            self.input_to_hidden_layer = nn.Linear(784,1000)\n",
        "            self.hidden_layer_activation = nn.ReLU()\n",
        "            self.hidden_to_output_layer = nn.Linear(1000,10)\n",
        "        def forward(self, x):\n",
        "            x = self.input_to_hidden_layer(x)\n",
        "            x1 = self.hidden_layer_activation(x)\n",
        "            x2= self.hidden_to_output_layer(x1)\n",
        "            return x2, x1\n",
        "    model = neuralnet().to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)[0]\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)[0]\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIKC4GCC0Q3r"
      },
      "source": [
        "def get_data():     \n",
        "    train = FMNISTDataset(tr_images, tr_targets)     \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets)     \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKRGmnNs0Q6l"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    model.eval()\n",
        "    prediction = model(x)[0]\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDCAaKZ90Yh0"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkcypD_k0Ykc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15f8aac2-fc23-4ad8-854c-28fb71dbb091"
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(100):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGzX7_vy0Ym3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "633568a4-9e32-4cd3-8a0c-532008ccd27c"
      },
      "source": [
        "epochs = np.arange(100)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with very small input values')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with very small input values')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tljOBe/fQGjVM2KFDvvddXUjBFOauQveg23qmroLexbhxJkwku3DzFT8vg+CKV7ly3oV9aqppML7nntz3KgpFKQjLCu55VBiqcYQojrA1az27nEQ7SxL6ypPQUPOcPeWIiIChQ+Gtt3LmfURE9vtYaghkNak4N+su84+yrHdB2wtc5Kezuz++MG4Tf6+Jji6Ye8mlU0m7bkp6K4ln0JI/9L7oN3T11Q/pZxHX6GvcrmMip+ilUcs1lPQ88zg6WrUWe/WVsx7SU+FVNT0sXC+t9EOOeEMqvqf7mnRSHTlS9f339Wz25UorhNM6gNmqe/fmeMeHnz1PDzSIVx0wQL+573NtVD/dp2vUH7BtMtbInEnKqt7e2hF8tQvkbmzOzNHw7GmgSrJdIb8tEA3LgdxcbR7u+ZDfVpVjGsbJXPHrV96nP9yRqBu6jdCNFZrr91ykrfg95/04qVU4livNOP7ShyMmaXTl4wV73qTrRB7OqVCTJqpRUVlhGys010tYnBWlJX/oXHrr9tD6qs2bq8bHq1aubB7AddepNmumaRHVtEft1Sqi+o/oGZoponruuapVq6qCplFRL+fzHLI8xz/MTni4MUY//qjav78Ja9pU9eyzzX6dOrru3nsL/d+yRsbPzRoZ/yhreufX66ewhbCrh1Jhu4qWpy2vxn4hQ6PZr0KGT6PRj4/0ADV0X0iMHqp/nurll6t+/LG+83aml84Izn6NTL2URfppxEA9HRqmWr26ru8+UvvH/KBXM08XVO6rGaEVzA3OOkv18ss19axamkZFfZCntT5J+iTjdQ+19ASVdAwvqJChoHoxS3QfNVVBD9Zvo53rbPbZ+y2E0xrCaQXVWLbrd3RWBd3Y9VbVjRtV09PNi5iZqbp5s+rMmXrs7IaqoG8yVKdyi54mRA8TpVsuulH12mtVr7xSdcQI1XXrzLV//aVau7ZqbKzqv/5lHsJll6keP27S/9//9ECDeE0hQjuyVEH1Zqargm7qMkz1ttuyBQ8PV336adVTp1RPnlT9+GPVq67SP++/v9D/MWtk/NyskfGP0qS39x5ReY+XCNbNZUz9qR1ER6vGVT+qtdittdijtdijEaTkKFhb8ocOZ5q+UXW07mz9d90eWl/X0lyfqvasfjD5gGpami69dZpuqtBMFTSNSno0prF+WbmPNmVdVlrd+VrTqKi/V4w3hWHv3qoNTSGsHTqozp+vumCBKVwHDlS9+GLV1q2zv8KrV1cdPVr1hhvM178r4ZgY1fvvV12+XPX0afPC7NunSR2uyYqTgej2dlerXnGFKui34ZfpP3hOT1FBj9Rupjp1qmq1auYec+eq7tmj779+WNvH7tZbmKZfh/XSk4RlpZUpolqlimpiYt4v7vHj+kfvR/QkYXqSMJ1adax5Znnx22/GWIJqr16qJ07kPL9njx6rda4eCqmhtzBNTxKmu1r1yDZyu3apvvaa6qZNXpMvyv/aGhk/N2tk/ONM6l3QgXfu1+RXYEZEZLtdyuIWHa3auPoBjWZ/HvEyNY4k7cFXekfkDH0i4mm9l2e1EjnHetRij/6H+1XHjdNfhr6st549N4fRANVm/KmJDNLfaZVdmHlsyUTqJhrpUapmhZ2qFKmakKB6442qnc1XvFaqpFqrltlv21b1mWdMgX/ddZoWWV1TCdcxPK8dWarJROof0lo/eP1Qdianp6tOn26+3N1laNjQGJm+fVWHD1edOVM1NTX7umPHTAE/b575UvdGZqbq7NmqTzyhumVLdthrr2UbqZ49VQ8fNuc2b1Zt08Z7BjRsqDpmjElr/HizrV/v/x9g+3bVnTv9j790qeq4cappad7Pb96ses45RrYmTVQPHfIezwul2ciUm0XL2rdvr3aCzPwpjN7eJuSDnL2pILsHTFwcXHEFfPFFzl5aBw8WvbdOoIkjiRtJZC+12ByVwLrQluw5XClLh6MHT3NX5JuMOfEvQjJPs4zzWcb5/Mnf2EEshyvXZdqdK+n21xswbx77M2rQIXMpSTTMusfVzON+nqWN/M5ZeiyXDEu4hD58wjGiaMBffM1lNCCJCiEKmZkAHAmpzmuZI5jJEG5hOqN5iVQi+LlyN/5+WxzUq8eGnTs5cqQp8+YpGYeOUT98H2dn7mXfqWpsiu5I1wc70vveJqbftIs//oDXXoPdu2HUKOjRw2Sai9272XHlCGJXfkYmwvYKDVn+wg8MuKt27oeZlgbz5sHZZ0PbtlC9+hnJI59s2MC6qVNp/vTTUMGt42xqKsyZA0ePwsmT5gXs1s3IJHl3eS52fvsNJkyA//wHmjXz+7KilGci8quqti/Uxf4QSAtWnJutyfhHQfXOa+BeSWwVOKV5DbiL5oBez7t6L89qPMuz/PT5bS1YrTO5UdPxaOQJCzNf88OGqT71lGlwBdULLlC9/no9GtPYe4I1a6redZemVamhG6RJVo3mJt7S04QYd84dd6hOnqy6ZInx/6ek6A93JOpJwnQV5+llfKk7qa0Hqa7zH19q3Ea7d6suWqRbzx+gpwlRxbh6Xuc2jau8N0dtMmDveGam6ptvqvboYb6+SxH2f11wsO4ya2TOJP7qXZoaxQeRqHPoo3/STE9RQdeEttJGbMoR51re1+WVOmmGhOS4eK/U0ve4Tl8Kv1/HRTyvA/hQG7DFMVSZ2pnvdB5XmfiRkapjx6pu22Z83++/r/rAA8b9EhNj4rRqpfrJJ6agdXHokGk7mDtX9eWXVefMMY2yqqo//qjpYeG6omJHHcvzqqC7WnZXTUnx+ewXPviVJksVVdDdoXX003+v9hpvzvNb9Klqz2o8vxaq63Z5JBh1VrVGxhqZUsTixYvzbUspzgb35qzVml7GCLi2AXyoCrqZhvpFRH9dc8V9mlalhu4nWjvznZ7FEX2bwaqgh2Nbqk6YYHzfu3YZn/+gQZp6zjmmncEt4T3UMu0XoPuoqb/1f1z1QD6NtwcPqmZkFPyhz5mjGuIYv6uvzt3o643ly1Vvusn0TCokwfiOB6POqtbIWCNTCsiumWR6nYfKfcLBQBiTqhxVdzdXVY7qVG5RBT1FBV0QdpUOr/J+jkbvBJbpcSrrD1yoTeu7FcwbN+qR2s30JGG6nbqaTqiuGvBEdk8cDxYvXmxqHk6N4+dhr+qHkUP1W7ro+Bov63vTjwc+A9591zSe+2rQDgDB9o6rBqfOqqXbyNhpZYIA19xWppFeUM15PjXVTDly4oT/c1GFcppWrKYjP9OQv9hMY9bQgo00ASCMdGpygKv5lAF8RDwr2UJD5tGbVbTjMR6nPttYe+X9tGgBPRMT6bnrM16pEs2UtKF8dLo37zGIvcQwuPIcJv4rPPvmjRsTtWYpDBpE7F9/wVs/0OaCC/IWWMQ0PCck0OHNBDq8OQqAi/1Tt+gMGmQ2iyXIsEamjOKtx9fgwd7n3vJnrip/4lTjMH2YR38+ojuLiMRYpMyQUEIyfU9k9UuFTjwbNoHmJ1YwUl4nXNOgcWOY+QMtOnUykZ5+Gr75hvApU7hrzkuM5nmOUZVhtb9i4rO1GDzYI9Hq1WHBAlPpKW09hCwWSxbWyJRBctZMcq6a52vSRU8E0xU2r4m4u7OQa/mQ+mwjTrbRPGQDIRmn2RlajxkZN7O+5kV0f6QjfUbHGSHWrIEtW8ysfmFhZra+Sy+lQ926dHAlevw4rFgB8fEQGZl9s9BQuOwyuOwyQvbsgcREzrrgAr66qFXeD8MaGIulVBNQIyMiY4FbAQX+AG4GagOzgGjgV+AmVT0lIncDtwPbgL5OWGdggKqODaScZY2CTNWeG6Ufc3iJ0SylEwP5AM/p0cM5wTM8yGj+y2Gqsb3iuVRp05yQy/pC377Ubd+eOz0L90aNzJYfkZHQpUvecc45B+69N/+0LBZLqSdg68mISF1gNNBeVVsBocD1wDPAC6raGDgM3OJcMhg4D/gJ6CkiAjwK/DNQMpYl3Nce2bq1cGnUYxuf0IePGYCgXMtsRvJajjit+Z1lnM9o/ssrFe7hyzd2cd7J5dT/5SPjkzv/fFt7sFgsfhPoRcsqAJVFpAIQAewGLgVmO+ffAvo6+wKEOfHSgRuB+ap6KMAylno8F6XKjxocpCIns47rsJNJ3MN6mtGdRUys9izfzkhiSXgvnucftGANAL35hKV0IpqDDKm1gGozJnH9zZUDpZbFYgkCAuYuU9WdIvIcxv11AvgK4x47oqqnnWg7gLrO/svA/4A1wI/AJ0DPQMlXFnA14nurudRhJ4/wL3ZTm6V0Yg0t6cUChjGDrnzLaULZzLkk0YCuLCGETN4NHcL+kdcz7uXLAPjo+AyS7zyP9xjEu9zAv3iEFSHt2friJ8z0Nk2IxWKxFJCAzV0mItWBj4DrgCPAh5gazOOOqwwRqYeprbTyuHYC8DuQCQwBtgP3qmqmR7wRwAiAmJiYhFmzZvktX0pKClWqVCmccgFk4cJaTJvWiL17KzkhuV1TtdjLt1xCI7ZQgdOEkJ2HG2lMIjcQSgZtKqzh3MyN/Jh5IVNr3kvP2zO44IItOfTe9to6hrxvuvPOq9Sflfc8wiWXJwdUx+KmtOZ1oAlGvYNRZyia3t26dSubc5cB1wLT3Y6HAJOBA0AFJ6wT8KXHdXWAz5z9bzFtOY8Bl+V1v7I2GNPbNPeQ/7xg1Tmov9Faj1NZr4n5TvXwYV30wAL9V7Vn9EJ+1Lj6mXnOhuxV70mTVJ99tnCj2csAJZ3XJUUw6h2MOquW8cGYInI18Ll61CL8YBtwgYhEYNxl3YHlwGLgGkwPs6EYt5g7/wQmOPuVMT3TMjFtNWUad/eXr67GviqWQibns4yXuYumbOCaSp8x6P+6QDW49JmeXPpMTx4urGCuxd8tFovlDONPm8x1wCQR+Qh4Q1XX+ZOwqv4sIrOBFcBpYCUwBfgcmCUiTzlh013XiEg759oVTtC7mK7P24H/+KVRKcVzbEtexiSeFSTwK5U5QSVOEssO+jKXeuzgBOHccfZsBr3QI/cARYvFYill5GtkVPVGETkLGATMEBEF3gTeU9U8nfeq+hjG1eXOFsgem+cRfyXZXZpR1UnApPxkLAt4G9viTid+4nZepxcLiGFfjnNpVOJLevJkxYn8/b9X88aIAK/LYbFYLGcIv7owq+oxTKP9LMxgyn7ACmcApSUPXONbfI1tacIGPqI/P3ERV/Mpi+jOTcwkjiSqc4gIUokglXviPqHrG0O41hoYi8VShvCnTaY3ZqR+Y2Am0EFV9zltLWuB/wZWxLKLp4sshAz+yaP0cZqhBKUJG0kjnEd5kuf5B6lE5lhh0jUnmcVisZRF/GmTGYAZof+de6CqporILT6usZDTRRbOCd7hRgbwMV/TgyNUA+BzruSN6vexPySGE4cgrr41LBaLpfzgj5F5HDNSHwARqQzEqGqSqi4KlGDlgW3bzG8NDjKP3nRiKWN4gRcZk1VLuX8w3F+yYlosFkvA8KdN5kPAvftyhhNmccN9brEGDcxx/frGRfYZV5HAr1zH+1kGJinJ1lYsFkv5x5+aTAVVPeU6UDM7csUAylTm8Db1/ogRMHQohE+bTKf0/3EjbzOba4mIMDUYi8ViCQb8qcnsdxr/ARCRPphR+0GPq/Zy4425uyenpsKnk7fzZPrDLArrybsMJi4OpkyxNRiLxRI8+FOTGQkkisjLmIm0tmOmiAlqPGsvuVFe5i5CyODu0Mm8/aZY42KxWIIOfwZjbsZMD1PFOU4JuFRlgJyDK5U+fMJjPEEY6SylEweJpg/zuI9n+TOtIePG2RqMxWIJPvya6l9ErgRaAuHiLFilqk8GUK5Sj6vnWDy/8gJjuZjv+ZPm/EVDrmE21TnCCtoxiTE54lssFksw4c9gzNcwk1N2A6ZhJrf8JcBylXrq14cDW1NYTDdSieB2XmM6t5BBBYRMmrGePZxDhvOI69cvYYEtFoulBPCn4f9CVR0CHFbVJzDT8zcNrFiln4kTYWDFTziLZK7lQ6ZwO5UiKjBqFFSOCGEdf+MIZgoY26PMYrEEK/4YmTTnN1VE6mCWRg76ZRMHD4YnW85iV2gsP3FRVs+xV181v3FxZjp/26PMYrEEM/60yXwqItWAZzHT9iswNaBSlQUOHSJ29ZnxA5sAABk7SURBVJcw9h4yns1pqwcPtkbFYrFYIJ+ajIiEAItU9YiqfgTEAc1VdUJe15U3vI3m5+OPIT0drr++hKWzWCyW0kueNRlVzRSRVwDXYmIngZPFIVhpwddo/ksbzqJ2kyYQH1+yAlosFkspxp82mUUiMkBcfZeDjHHjQFNTeZk7uRQzH2jV1D3UWrOYF/ddT+K7QflYLBaLxS/8aZO5HfgHcFpE0jCj/lVVzwqoZKWEbdtgPM9xJ68yislM4EmSqUoombx+9Hq2jjDxbBuMxWKx5MafEf9Vi0OQ0kZioqnFxOo2HuLfzKEvx4nkKR4lnQr8xnn8SQtIxY7mt1gsFh/4MxjzYm/hnouYlSfc22He5UEEZQyT2EZ9fqAzL3IP08ler82O5rdYLBbv+OMuc19TKxzoAPwKXBoQiUoBrnnJOvM9g5jFE0xgG3EAvM5IZjKEE1TOim9H81ssFot3/HGXXe1+LCL1gEkBk6gUsG2bWWxsEmPYTizP8GDWuYgISE2NyHFsR/NbLBaLd/zpXebJDuBv+UUSkWYissptOyYiY0Skhoh8LSIbnd/qTvwBIrJGRL4XkWgn7FwReb8QMhaJ+vUhnhUksILHeIITGKPiGr1vR/NbLBaLf/jTJvNfzCh/MEapLWbkf56o6nonLiISCuwE5gAPYQZ4/ltEHnKOHwTuBs4H+gM3AP8FngLGF0ylojNxInw9fBOcgp/pCGTXWOxofovFYvEff9pklrvtnwbeU9UfC3if7sBmVd3qrKzZ1Ql/C1iCMTKZQCXMjM/pItIF2KOqGwt4ryIzeDC0/GQLfAhJNCQuLtvAWCwWi8V/RFXzjiASCaSpaoZzHApUUlWfa0J6SeMNYIWqviwiR1S1mhMumNmdq4nIZcC/gV3AjcCHwPWqeiiPdEcAIwBiYmISZs2a5a9IpKSkUKVKFZ/nmz3zDDWWLWPp7Nl+p1kWyE/v8kgw6gzBqXcw6gxF07tbt26/qmr7MyxSNqqa5wb8D6jidlwF+Cm/69ziVwQOADHO8RGP84e9XDMEGANcAMzGTMgZkdd9EhIStCAsXrw47wiXXKLauXOB0iwL5Kt3OSQYdVYNTr2DUWfVoukNLFc/y/PCbP40/Ier25LLzn5EHvE9uRxTi9nrHO8VkdoAzu8+98giEgEMA14BngCGAj8Axeus2rwZGjUq1ltaLBZLecMfI3NcRLJmgRSRBOBEAe4xCHjP7XgexnDg/H7iEf9+4CVVTQcqYzodZFIww1Y00tJg505rZCwWi6WI+NPwPwb4UER2YeYtOwe4zp/EnfacyzDzn7n4N/CBiNwCbAUGusWvA3RQswInmB5my4AjQF9/7nlGSEoCVTj33GK7pcVisZRH/BmMuUxEmgPNnKD1Ti0jX1T1OBDtEXYQ09vMW/xdwJVuxx9iOgAUL1u2mF9bk7GUUdLT06lSpQp//vlnSYtSrERFRQWdzuCf3uHh4cTGxhIWFlZMUhn8GSdzJ5Coqqud4+oiMkhVXw24dCXF5s3m19ZkLGWUHTt2EBMTQ2xsLMG0SkdycjJVqwbfnL756a2qHDx4kB07dtCwYcNilMy/NpnbVPWI60BVDwO3BU6kUsCWLRAZCbVqlbQkFkuhSEtLIyoqKqgMjMU3IkJ0dDRpaWnFfm9/jEyo+4JlzjiZioETqRSwZYtxldk/qKUMYw2MxZ2Seh/8MTILgPdFpLuIdMf0FJsfWLFKGNt92WIpEgcPHqRt27a0bduWc845h7p162Ydnzp1Ks9rly9fzujRo/O9x4UXXnimxLUEEH+MzIPAN8BIZ/sD3Oa5L2+oZtdkLJYgITERGjSAkBDzm5hYtPSio6NZtWoVq1atYuTIkYwdOzbruGLFipw+fdrnte3bt+ell17K9x4//fRT0YQsATIyMkpahGInXyOjqpnAz0ASZi2ZS4Hy231jzx44ccI2+luCBtcifVu3mm+srVvNcVENjSfDhg1j5MiRdOzYkQceeIBffvmFTp060a5dOy688ELWr18PwJIlS7jqqqsAePzxxxk+fDhdu3alUaNGOYyPaxqVJUuW0LVrV6655hoSEhIYPHiwa+YQvvjiC5o3b05CQgKjR4/OStedpKQkunTpQnx8PPHx8TmM1zPPPEPr1q1p06YNDz30EACbNm2iR48etGnThvj4eDZv3pxDZoC77rqLGTNmANCgQQMefPBB4uPj+fDDD5k6dSrnn38+bdq0YcCAAaSmmhm69u7dS79+/WjTpg1t2rThp59+YsKECUyalL2yyrhx43jxxReLnBfFic/eZSLSFDOQchBmWpj3AVS1W/GIVkLY7suWIMO1SJ87qQFaVnzHjh389NNPhIaGcuzYMb7//nsqVKjAwoULeeSRR/joo49yXbNu3ToWL15McnIyzZo1Y9SoUbm64a5cuZI1a9ZQtWpVevXqxY8//kj79u25/fbb+e6772jYsCGDBg3yKlOtWrX4+uuvCQ8PZ+PGjQwaNIjly5czf/58PvnkE37++WciIiI4dMhMozh48GAeeugh+vXrR1paGpmZmWzfvj1PvaOjo1mxwkxef/DgQW67zfSdGj9+PNOnT+fuu+9m9OjRXHLJJcyZM4eMjAxSUlKoU6cO/fv3Z8yYMWRmZjJr1ix++eWXAj/3kiSvLszrgO+Bq1R1E4CIjC0WqUoS233ZEmT4Wj48EMuKX3vttYSGhgJw9OhRhg4dysaNGxER0tO9D7+78sorqVSpEpUqVaJWrVrs3buX2NjYHHE6dOhAbGwsycnJtG3blqSkJKpUqUKjRo2yuuwOGjSIKVOm5Eo/PT2du+66i1WrVhEaGsqGDRsAWLhwITfffDMREWaykRo1apCcnMzOnTvp168fYMae+MN112WPX1+9ejXjx4/nyJEjpKSk0LNnTwC++eYbZs6cCUBoaChRUVFERUURHR3NypUr2bt3L+3atSM6OtrrPUoreRmZ/sD1wGIRWQDMwoz4L99s2ZK9IpnFEgTUr29cZN7CzzSRkZFZ+48++ijdunVjzpw5JCUl0bVrV6/XVKpUKWs/NDTUa3uOP3F88cILLxATE8Nvv/1GZmam34bDnQoVKpCZmZl17NlV2F3vYcOGMXfuXNq0acOMGTNYsmRJnmnfeuutzJgxgz179jB8+PACy1bS+GyTUdW5qno90BxYjJleppaITBaRvxeXgMXO5s1Qrx64vbQWS3lm4kSzKJ87xbGs+NGjR6lbty5AVvvFmaRZs2Zs2bKFpKQkAN5/3/siu0ePHqV27dqEhITw9ttvZzXOX3bZZbz55ptZbSaHDh2iatWqxMbGMnfuXABOnjxJamoqcXFxrF27lpMnT3LkyBEWLVrkU67k5GRq165Neno6iW4NX927d2fy5MmA6SBw9OhRAPr168eCBQtYtmxZVq2nLOFPw/9xVX1XVa8GYoGV4LbofXnD9iyzBBmDB5fMsuIPPPAADz/8MO3atStQzcNfKleuzKuvvkqvXr1ISEigatWqREVF5Yp3xx138NZbb9GmTRvWrVuXVevo1asXvXv3pn379rRt25bnnnsOgLfffpuXXnqJ8847jwsvvJA9e/ZQr149Bg4cSKtWrRg4cCDt2rXzKdc///lPOnbsyEUXXUTz5s2zwl988UUWL15M69atSUhIYO3atQBUrFiRbt26MXDgwCxXY5kikOsIFOdW1PVk3nlHNS5OdTcxOityuL7zToGSKzME43obwajz2rVr9dixYyUtRrHjqXNycrKqqmZmZuqoUaP0+eefLwmxikRGRoa2adNGN2zY4DOOv3m9du3aXGGUgvVkyj2uLpz7tx7nHPby2/FzA9KF02KxFC9Tp06lbdu2tGzZkqNHj3L77bfnf1EpYu3atTRu3Jju3bvTpEmTkhanUPgz1X+5x9WFsxWm+/IWGgWsC6fFYik+xo4dy9ixZbdTbIsWLdjiGlZRRrE1GbK7ajbFdF3cROMc4RaLxWIpHNbIkN1VsyVryET4k7/lCLdYLBZL4bBGhuwunK1YzV80JJXIYunCabFYLOUd2yZDdrtL25vXsDq9FXFxxsDY9hiLxWIpGrYm4zD42lM01Q30ebglSUnWwFgsRaFbt258+eWXOcImTZrEqFGjfF7TtWtXli9fDsAVV1zBkSNHcsV5/PHHs8ar+GLu3LlZY0wAJkyYwMKFCwsivuUMYo2Miw0b4PRpaNWqpCWxWMo8gwYNYtasWTnCZs2a5XOSSk+++OILqlWrVqh7exqZJ598kh49ehQqrZKiPC0JYI2MizVrzG/LliUrh8VSDrjmmmv4/PPPsxYoS0pKYteuXXTp0oVRo0bRvn17WrZsyWOPPeb1+gYNGnDgwAEAJk6cSNOmTencuXPWcgCA1ynzf/75Z+bNm8f9999P27Zt2bx5M8OGDWP27NkALFq0iHbt2tG6dWuGDx/OyZMns+732GOPER8fT+vWrVm3bl0umUrzkgBPPfVUqV0SIKBtMiJSDZgGtAIUGA6sxywb0ACzRs1AVT0sIgOAJ4FDQF9VPSgi5wL/UtXrvCR/Zlm9GkJDoVmzgN/KYilWxoyBVavObJpt24JboeZJjRo16NChA/Pnz6dPnz7MmjWLgQMHIiJMnDiRGjVqkJGRQffu3fn9998577zzvKbz66+/MmvWLFatWsXp06eJj48nISEBgP79++eaMn/YsGH07t2bq666imuuuSZHWmlpaQwbNoxFixbRtGlThgwZwuTJkxkzZgwANWvWZMWKFbz66qs899xzTJs2Lcf1pXlJgLPOOoshQ4aUyiUBAl2TeRFYoKrNgTaYxc4eAhapahNgkXMMcDdwPvA6cIMT9hQwPsAyGtasgcaNoRAzsFoslty4u8zcXWUffPAB8fHxtGvXjjVr1uRwbXny/fff069fPyIiIjjrrLPo3bt31rnVq1fTpUsXWrduTWJiImtc3ggfrF+/noYNG9K0aVMAhg4dynfffZd1vn///gAkJCRkTarpTnp6OrfddhutW7fm2muvzZLb3yUBIjxnIfWC55IA3vT75ptvstq2XEsCxMXFZS0J8NVXX5WqJQECVpMRkSjgYmAYgKqeAk6JSB+gqxPtLWAJZsLNTKASEAGki0gXYI+qbgyUjDlYvRp8fE1ZLGWaPGocgaRPnz6MHTuWFStWkJqaSkJCAn/99RfPPfccy5Yto3r16gwbNizXtPj+UtAp8/PDtVyAr6UC7JIAhSOQNZmGwH7gTRFZKSLTRCQSiFHV3U6cPUCMs/80sBC4GngPeBT4ZwDlyyYtzUzxb9tjLJYzRpUqVejWrRvDhw/PqsUcO3aMyMhIoqKi2Lt3L/Pnz88zjYsvvpi5c+dy4sQJkpOT+fTTT7PO+Zoyv2rVqiQnJ+dKq1mzZiQlJbFp0ybAzKZ8ySWX+K2PXRKgcASyTaYCEA/crao/i8iLZLvGAFBVFRF19r8GvgYQkSHAF0BTEbkPOAzco6o5FokVkRHACICYmJgCfcmkpKRkxa+yaRPtMzNZI8L+In4NlXbc9Q4WglHnqKgoMjIyvBa2xUnfvn254YYbmD59OsnJyTRq1IhWrVrRtGlTYmNj6dixI2lpaSQnJ5ORkcHx48dJTk5GVUlJSaFJkyb07duX1q1bc/bZZ9O2bVtOnjxJcnIy48aNo0OHDkRHR9O+fXtSUlLIyMigd+/e3H333UyaNImZM2eSnp7OiRMnSE9P55VXXmHAgAFZ7TuDBw/Ocb9KlSpx/Phxr89uyJAh3HTTTcyYMYMePXoQGRlJcnIyF110ET179iQ+Pp6KFSvy97//ncceeyyrvWf8+PGEhYXx1ltv0bBhQ/r27UuLFi2Ii4ujdevWWfq7ywB41S85OZmJEycyevRopk6dSmhoKM8//zzt27fn5MmTdO7cmaioqCyD50laWlrx/xcCNb0zcA6Q5HbcBfgc0/Bf2wmrDaz3uC4C+AYIA74EIoGhwG153a9IU/2//bYqqK5ZU6A0yiLBOO19MOpsp/oPLo4dO+bXkgDlaqp/Vd0DbBcRV3et7sBaYJ5jNHB+P/G49H7gJVVNBypjeqVlOsYnMKxZA2FhUEan0rZYLMHNunXrSu2SAIGeVuZuIFFEKgJbgJsx7UAfiMgtwFZgoCuyiNQBOqjqE07Qf4FlwBGgb8CkXL3adF0OCwvYLSwWiyVQNG/evNQuCRBQI6Oqq4D2Xk519xF/F3Cl2/GHwIeBkc4sSjZuHHyzdQ2rIzqSnGink7FYLJYzSdCO+F+4sBYjRsCBrSk04i9+SW1pV8O0lCuMu91iMZTU+xC0RmbaNLP65d/4E4DVtMpaDdNiKeuEh4dz9OhRa2gsgDEwBw8eLNTYnqIStFP979tnugm6VsNcgxkjY1fDtJQHYmNj+e2330hJSSlpUYqVtLS0EilISxp/9A4PDyc2NraYJMomaI1MrVon2bs3nHcZzFf8nYOYKRjsapiW8kBYWBgpKSm0b++tSbT8smTJEtq1a1fSYhQ7pVnvoHWX3XrrFlxTCR3gbJQQuxqmxWKxnGGC1sj06LGPKVMgLg5EzO+UKbZ3mcVisZxJgtZdBsagWKNisVgsgUPKS+8TEdmPGdzpLzWBAwESpzQTjHoHo84QnHoHo85QNL3jVPXsMymMO+XGyBQUEVmuqsHVKkpw6h2MOkNw6h2MOkPp1jto22QsFovFEniskbFYLBZLwAhmIzOlpAUoIYJR72DUGYJT72DUGUqx3kHbJmOxWCyWwBPMNRmLxWKxBJigNDIi0ktE1ovIJhF5KP8ryh4iUk9EFovIWhFZIyL3OOE1RORrEdno/FYvaVkDgYiEishKEfnMOW4oIj87ef6+s8ZRuUFEqonIbBFZJyJ/ikinYMhrERnrvN+rReQ9EQkvb3ktIm+IyD4RWe0W5jVvxfCSo/vvIhJfcpIbgs7IiEgo8ApwOdACGCQiLUpWqoBwGrhXVVsAFwB3Ono+BCxS1SbAIue4PHIPOFNsG54BXlDVxsBh4JYSkSpwvAgsUNXmQBuM7uU6r0WkLjAaaK+qrYBQ4HrKX17PAHp5hPnK28uBJs42AphcTDL6JOiMDNAB2KSqW1T1FDAL6FPCMp1xVHW3qq5w9pMxhU5djK5vOdHeIpArjpYQIhKLWfxumnMswKXAbCdKudJbRKKAi4HpAKp6SlWPEAR5jZm1pLKIVMAs0b6bcpbXqvodcMgj2Ffe9gFmquF/QDURqV08knonGI1MXWC72/EOJ6zcIiINgHbAz0CMqu52Tu0BYkpIrEAyCXgAyHSOo4EjqnraOS5ved4Q2A+86bgIp4lIJOU8r1V1J/AcsA1jXI4Cv1K+89qFr7wtdeVbMBqZoEJEqgAfAWNU9Zj7OTVdC8tV90IRuQrYp6q/lrQsxUgFIB6YrKrtgON4uMbKaV5Xx3y5NwTqAJHkdiuVe0p73gajkdkJ1HM7jnXCyh0iEoYxMImq+rETvNdVfXZ+95WUfAHiIqC3iCRhXKGXYtorqjkuFSh/eb4D2KGqPzvHszFGp7zndQ/gL1Xdr6rpwMeY/C/Pee3CV96WuvItGI3MMqCJ0wOlIqahcF4Jy3TGcdohpgN/qurzbqfmAUOd/aHAJ8UtWyBR1YdVNVZVG2Dy9htVHQwsBq5xopUrvVV1D7BdRJo5Qd2BtZTzvMa4yS4QkQjnfXfpXW7z2g1feTsPGOL0MrsAOOrmVisRgnIwpohcgfHbhwJvqGq5W6pMRDoD3wN/kN028QimXeYDoD5m1uqBqurZqFguEJGuwH2qepWINMLUbGoAK4EbVfVkScp3JhGRtpiODhWBLcDNmI/Icp3XIvIEcB2mN+VK4FZMG0S5yWsReQ/oiplpeS/wGDAXL3nrGNuXMW7DVOBmVV1eEnK7CEojY7FYLJbiIRjdZRaLxWIpJqyRsVgsFkvAsEbGYrFYLAHDGhmLxWKxBAxrZCwWi8USMKyRsVjyQUQyRGSV23bGJpoUkQbus+taLOWNCvlHsViCnhOq2rakhbBYyiK2JmOxFBIRSRKR/4jIHyLyi4g0dsIbiMg3znoei0SkvhMeIyJzROQ3Z7vQSSpURKY666J8JSKVnfijxawH9LuIzCohNS2WImGNjMWSP5U93GXXuZ07qqqtMaOsJzlh/wXeUtXzgETgJSf8JeBbVW2DmVtsjRPeBHhFVVsCR4ABTvhDQDsnnZGBUs5iCSR2xL/Fkg8ikqKqVbyEJwGXquoWZzLSPaoaLSIHgNqqmu6E71bVmiKyH4h1n+LEWYbha2fxKUTkQSBMVZ8SkQVACmYKkbmqmhJgVS2WM46tyVgsRUN97BcE93m1MshuK70Ss4prPLDMbWZhi6XMYI2MxVI0rnP7Xers/4SZARpgMGaiUjDL5I4Cswy4s6KlV0QkBKinqouBB4EoIFdtymIp7dgvI4slfyqLyCq34wWq6urGXF1EfsfURgY5YXdjVqm8H7Ni5c1O+D3AFBG5BVNjGYVZ0dEbocA7jiES4CVnSWWLpUxh22QslkLitMm0V9UDJS2LxVJase4yi8VisQQMW5OxWCwWS8CwNRmLxWKxBAxrZCwWi8USMKyRsVgsFkvAsEbGYrFYLAHDGhmLxWKxBAxrZCwWi8USMP4fzDBdSTf3f00AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAa9ytf05_fs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "02131268-7458-4ac2-ac33-63eecff0b54e"
      },
      "source": [
        "plt.hist(model(x)[1].cpu().detach().numpy().flatten())\n",
        "plt.title(\"Hidden layer node values' distribution\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, \"Hidden layer node values' distribution\")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rY1MgWKhUV7M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f4242001-d2cc-4a9b-eb2a-b18c0baf3296"
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOLSIysxUWYs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-Gq2MLRUWbs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDSvGF6F5_iU"
      },
      "source": [
        "### With BatchNormalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB4QQE2Q5_k6"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/(255*10000)\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    class neuralnet(nn.Module):\n",
        "        def __init__(self):\n",
        "            super().__init__()\n",
        "            self.input_to_hidden_layer = nn.Linear(784,1000)\n",
        "            self.batch_norm = nn.BatchNorm1d(1000)\n",
        "            self.hidden_layer_activation = nn.ReLU()\n",
        "            self.hidden_to_output_layer = nn.Linear(1000,10)\n",
        "        def forward(self, x):\n",
        "            x = self.input_to_hidden_layer(x)\n",
        "            x0 = self.batch_norm(x)\n",
        "            x1 = self.hidden_layer_activation(x0)\n",
        "            x2= self.hidden_to_output_layer(x1)\n",
        "            return x2, x1\n",
        "    model = neuralnet().to(device)\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)[0]\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)[0]\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axYwCbYF6o4X"
      },
      "source": [
        "def val_loss(x, y, model):\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)[0]\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dVibfKA5_nK"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fm9CMO7Z5_px",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0e222ca-80b4-4faf-916e-2c4da49c9e5d"
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(100):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSkAPTGh6ZFa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "bd0f9581-6567-4ec5-8498-065142be5a0b"
      },
      "source": [
        "epochs = np.arange(100)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with very small input values and batch normalization')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with very small input values and batch normalization')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvxnclR16ZJZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "fc6a2423-1b9a-4fe1-ef93-b0602c34be55"
      },
      "source": [
        "plt.hist(model(x)[1].cpu().detach().numpy().flatten())\n",
        "plt.title(\"Hidden layer node values' distribution with batch normalization\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, \"Hidden layer node values' distribution with batch normalization\")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yShRnRmN6ZS2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d3700377-bfdb-4d64-b152-f750fc8e9a54"
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2cfKrRG6ZV_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HpqnjA85_r6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laPDKyDg0Ypk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vsXfrfkuU0C"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}
